Using Wavelet Transforms to Detect Small-Scale Features within the Tuscaloosa Marine Shale, Louisiana & Mississippi

Abstract

The Tuscaloosa Marine Shale (TMS) is an unconventional play of central Louisiana and southwestern Mississippi. Previous studies divide the TMS into an upper low resistivity section and a lower high resistivity section or an upper calcite poor section, middle calcite rich section and a basal siliceous section. On the basis of core, TMS has been found to consist of different facies on very small scales, which are indiscernible from the open-hole wireline logs. Cores are not acquired in each and every well and therefore there is a need of a technique that could detect features hidden in the wireline logs in the absence of core data. In this study, the continuous wavelet transformation (CWT) technique is used to achieve this objective. This method uses wavelets to detect abrupt shifts in the data that may not be very obvious otherwise. Here, Paul4 wavelet is used to match the sonic (DT) and the deep resistivity (Rt) data and determine zones where the correlation coefficients are high. Results show that the wavelet analysis is able to detect power in both the DT and the Rt logs in all of the wells used in this study. Mostly, the power is detected along the same depths in both DT and Rt, possibly indicating layers differing in characteristics from adjacent layers. It is difficult to correlate these layers on the basis of DT and Rt alone across the study area. For detailed and accurate stratigraphic correlation of each layer, well logs with complete logging suites, mud logs and cores are needed. This detailed work in future, can help validate the results of the wavelet transformation technique as well as define the character of each possible layer identified using this technique.